A Practical Introduction to Econometric Methods: Classical and Modern
$9.99
A Practical Introduction to Econometric Methods: Classical and Modern
By Patrick K. Watson, Sonja S. Teelucksingh
US$ 9.99
Book Description

Table of Contents
  • Contents
  • Foreword
  • Preface
  • Introduction
    • Introduction
    • Classical and Modern Econometrics
    • Exercises
  • Part I: Classical
    • Chapter 1: The General Linear Regression Model
      • Models in Economics and Econometrics
      • Data and Econometric Models
      • Specifying the Model
      • Introducing the Error Term
      • Desirable Properties of the Error Term
      • The General Linear Regression Model
      • Ordinary Least Squares
        • Special Case: k = 2 and x1t = 1 for all t
      • Numerical Calculation
      • Forecasting with Econometric Models
      • The Gauss-Markov Theorem on Least Squares
        • Proof of Part (1) of the Theorem
        • Proof of Part (2) of the Theorem
        • Proof of Part (3) of the Theorem
      • Understanding the Lessons of the Gauss-Markov Theorem
      • Exercises
      • Appendix 1.1: Moments of First and Second Order of Random Variables and Random Vectors
        • Random Variables
        • Random Matrices and Vectors
        • Application to the General Linear Regression Model
      • Appendix 1.2: Time Series Data for Trinidad and Tobago 1967-1991
    • Chapter 2: Evaluating the Ordinary Least Squares (OLS) Regression Fit
      • Some Preliminary Remarks
      • The Coefficient of Determination and the Adjusted Coefficient of Determination
      • Confidence Intervals for Coefficients
      • Significance Tests of Coefficients
      • Testing the Simultaneous Nullity of the Slope Coefficients
      • “Economic” Evaluation of Regression Results
      • Reporting Regression Results
      • Exercises
    • Chapter 3: Some Issues in the Application of the General Linear Regression Model
      • Multicollinearity: the Problem
      • Multicollinearity: Detection
      • Multicollinearity: a Solution?
      • Multicollinearity: an Illustration
      • Misspecification
      • Dummy Variables
      • Illustration Involving a Dummy Variable
      • Exercises
    • Chapter 4: Generalized Least Squares, Heteroscedasticity and Autocorrelation
      • Generalized Least Squares
      • Properties of the Generalized Least Squares Estimator
      • Consequences of Using Ordinary Least Squares When u ~ (0, s2V)
      • GLS Estimation: a Practical Solution?
      • Ad Hoc Procedures for the Identification of Heteroscedasticity and Autocorrelation
        • Heteroscedasticity: Some Further Considerations
        • Heteroscedasticity: Testing for its Presence
          • The Goldfeld-Quandt Test
          • The Koenker Test
          • Illustration of the Koenker Test for Heteroscedasticity
        • Other Tests for Heteroscedasticity
        • Estimation in the Presence of Heteroscedasticity
        • Autocorrelation: The Problem
        • Autocorrelation: Testing for its Presence Using the Durbin-Watson Statistic
        • Some Justification for the Mechanism of the Durbin-Watson Test
        • An Illustration of the Durbin-Watson Test for Autocorrelation
        • Other Tests for Autocorrelation
        • Estimation in the Presence of Autocorrelation
          • The Cochrane-Orcutt Procedure
          • The Hildreth-Lu Procedure
          • The EViews Procedure
      • Autocorrelation and Model Specification: a Word of Caution
      • Exercises
    • Chapter 5: Introduction to Dynamic Models
      • Dynamic Models
      • Almon’s Polynomial Distributed Lag (PDL) Scheme
        • Illustration of Almon’s Polynomial Distributed Lag Scheme
      • The Koyck Transformation
        • Illustration of the Koyck Transformation
      • The Partial Adjustment Model
      • The Adaptive Expectations Model
      • Error Correction Mechanism (ECM) Models
        • Illustration of the Error Correction Mechanism Model
      • Autoregressive Distributed Lag (ADL) Models
        • Illustration of the Autoregressive Distributed Lag Model
      • The Durbin Test for Autocorrelation in the Presence of Lagged Endogenous Variables
        • Illustration of the Durbin h-Test
      • Exercises
    • Chapter 6: The Instrumental Variable Estimator
      • Introduction
      • Consistent Estimators
      • Is OLS Consistent?
      • The Instrumental Variable Estimator
      • The Errors in Variables Model
      • Exercises
    • Chapter 7: The Econometrics of Simultaneous Equation Systems
      • Introduction
      • Identification
      • Identifiability of an Equation and Restrictions on the Structural Form
        • Conditions of Identifiability of an Equation
      • Estimation in Simultaneous Equation Models
        • Consistency of the Two Stage Least Squares Estimator
        • The Two Stage Least Squares Estimator as an Instrumental Variable Estimator
        • Equivalence of Two Stage Least Squares and Indirect Least Squares in the Case of an Exactly Identified Equation
        • Illustration of the Two Stage Least Squares Estimator
      • Exercises
    • Chapter 8: Simulation of Econometric Models
      • Introduction
      • Dynamic and Static Simulation
      • Some Useful Summary Statistics
        • Root Mean Square Error
        • Mean Absolute (or Mean Difference) Error
        • The Theil Inequality Coefficient
        • The Theil Decomposition
        • Regression and Correlation Measures
      • Some Illustrations of the Use of Model Simulation
        • Evaluation of Goodness-of-Fit of Single Equation Systems
        • Forecasting with Single Equation Systems
        • Evaluation of Goodness-of-Fit of Multiple Equation Systems
      • Dynamic Response (Multiplier Analysis) in Multiple Equation Systems
        • Illustration of Dynamic Response
        • Forecasting and Policy Simulations with Multiple Equation Systems
        • Illustration
      • Exercise
  • Part II: Modern
    • Chapter 9: Maximum Likelihood Estimation
      • Introduction
      • The Cramer-Rao Lower Bound (CRLB)
      • Properties of Maximum Likelihood Estimators
      • Maximum Likelihood Estimation in the General Linear Regression Model
      • Exercises
    • Chapter 10: The Wald, Likelihood Ratio and Lagrange Multiplier Tests
      • Introduction
      • Defining Restrictions on the Parameter Space
      • The Likelihood Ratio Test
      • The Wald Test
      • The Lagrange Multiplier Test
        • Illustration: Test of Parameter Redundancy
        • Illustration: Testing Restrictions on Coefficient Values
      • Conclusion
      • Exercises
    • Chapter 11: Specification (and Other) Tests of Model Authenticity
      • Introduction
      • Ramsey’s RESET Test for Misspecification (Due to Unknown Omitted Variables)
        • Illustration of the Ramsey RESET Test
      • The Jarque-Bera Test for Normality
        • Illustration of the Jarque-Bera Test for Normality
      • The Ljung-Box and Box-Pierce Tests for White Noise
        • Illustration of the Ljung-Box Test
      • The White Test for Heteroscedasticity
        • Illustration of the White Heteroscedasticity Test
      • The Breusch-Godfrey Test for Serial Correlation
        • Illustration of the Breusch-Godfrey Test for Serial Correlation
      • The Chow Test for Structural Breaks
        • Illustration of the Chow Test for Structural Breaks
      • Exercises
    • Chapter 12: Stationarity and Unit Roots
      • The Concept of Stationarity
      • Unit Roots: Definition
      • Looking for Unit Roots: an Informal Approach
      • Formal Testing for Unit Roots
      • Exercises
    • Chapter 13: An Introduction to ARIMA Modelling
      • Introduction
      • ARIMA Models
        • Autoregressive Processes of Order p AR(p)
        • Moving Average Processes of Order q MA(q)
        • Autoregressive Moving Average Processes of Order p, q ARMA(p, q)
        • Autoregressive Integrated Moving Average Processes of Order p, d, q ARIMA(p, d, q)
      • The Partial Autocorrelation Function (PACF)
      • Estimating the Autocorrelation and Partial Autocorrelation Functions
        • Estimation of the Mean
        • Estimation of the Autocovariance of Order k
        • Estimation of the Autocorrelation of Order k
        • Estimation of the Partial Autocorrelation of Order j
        • Sampling Distributions of and
      • The Box-Jenkins Iterative Cycle
        • Identification
        • Illustrating the Identification of p and q
        • Estimation and Diagnostic Checking
        • Illustration of the Estimation and Diagnostic Checking Phases
        • Forecasting
        • Illustration of the Forecasting Phase
      • Seasonal Models
      • Exercises
      • Appendix 13.1
    • Chapter 14: Vector Autoregression (VAR) Modelling with Some Applications
      • Introduction
      • Vector AutoregressiON Models
      • Illustration of vector autoregression estimation using eviews
      • Evaluation of Vector Autoregression Models
        • The Impulse Response Function
        • Variance Decomposition
      • Forecasting with Vector Autoregression Models
        • Illustration of Forecasting with Vector Autoregression Models
      • Vector Autoregression Modelling and Causality Testing
      • Testing for Causality
        • Direct Granger Tests
        • Illustration of Direct Granger Tests
        • The Sims Test
      • Exercises
      • Appendix 14.1
    • Chapter 15: Cointegration
      • Introduction
      • The Vector Error Correction Model (VECM)
      • The Engle-Granger (EG) Two-Step Procedure
        • Illustration of the Engle-Granger Two-Step Procedure
        • Strengths and Weaknesses of the Engle-Granger Two-Step Procedure
      • The Johansen Procedure
        • Estimation of a and b
        • Testing for the Cointegrating Rank r
        • Illustration of the Johansen Procedure
      • Cointegration and Causality
      • Exercises
      • Appendix 15.1: Critical values for ADF tests of cointegratability
        • (constant term included in test equations)
  • Appendices
    • Statistical Tables
  • References
  • Index
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